{"id":3913,"date":"2019-01-09T16:29:47","date_gmt":"2019-01-09T10:59:47","guid":{"rendered":"https:\/\/www.delveinsight.com\/blog\/?p=3913"},"modified":"2023-06-14T12:09:00","modified_gmt":"2023-06-14T06:39:00","slug":"artificial-intelligence-face-scanning-app-spots-signs-of-rare-genetic-disorders","status":"publish","type":"post","link":"https:\/\/www.delveinsight.com\/blog\/artificial-intelligence-face-scanning-app-spots-signs-of-rare-genetic-disorders","title":{"rendered":"Artificial Intelligence face-scanning app spots signs of rare genetic disorders\ufeff"},"content":{"rendered":"\n<p><br><\/p>\n\n\n\n<p>A range of <strong>rare genetic disorders <\/strong>is pinpointed by a <strong>deep-learning algorithm<\/strong>. This helps doctors and researchers by analysing pictures of people\u2019s faces. Researchers describe the technology after the diagnostic aid, a smartphone app called <strong>Face2Gene<\/strong>. It relies on <strong><a href=\"https:\/\/www.delveinsight.com\/blog\/machine-learning-models-to-help-predicting-cancer-symptoms-plan-treatment\/\">machine-learning algorithms<\/a><\/strong>and <a href=\"https:\/\/www.delveinsight.com\/blog\/machine-learning-models-to-help-predicting-cancer-symptoms-plan-treatment\/\">brain-like neural networks <\/a> . This is for classification of distinctive facial features in people\u2019s photos with <strong><a href=\"https:\/\/www.delveinsight.com\/report-store\/congenital-heart-disease-pipeline-insight\">congenital <\/a><\/strong>and <strong>neurodevelopmental disorders<\/strong>. The patterns from the pictures are used for inference, the model homes in on possible diagnoses and provides a list of likely options. The technology is not intended to provide definitive diagnoses, albeit used by doctors as an aid. However it raises a number of ethical and legal concerns. Researchers at <strong>FDNA<\/strong>, a digital-health company in <strong>Boston, Massachusetts<\/strong>, first trained the <strong>artificial intelligence (AI) <\/strong>system to differentiate<strong> Cornelia de Lange syndrome and Angelman syndrome<\/strong>. These are two conditions with distinct facial features. &nbsp;<strong>FDNA chief technology officer, Yaron Gurovich,<\/strong> fed the algorithm of more than <strong>17,000 images<\/strong> of diagnosed cases across <strong>216 <\/strong>distinct syndromes. When new images of people\u2019s faces were presented, the best diagnostic guess of the app was correct in about 65% of cases. And when multiple predictions were done, Face2Gene&#8217;s top-ten list contained the right diagnosis about 90% of the time.<\/p>\n\n\n\n<p><strong>Narrowing\nthe field<\/strong><\/p>\n\n\n\n<p>FDNA\nintends to help other companies filter, prioritize and interpret genetic\nvariants of unknown significance during DNA analysis by developing this\ntechnology. FDNA needs data to train its models.So, the Face2Gene app\nis currently available for free to healthcare professionals. Majority of whom\nuse the system as a kind of second opinion for diagnosing rarely seen genetic\ndisorders. A starting point can also be provided in cases in which a doctor\ndoesn\u2019t know what to make of a patient\u2019s symptoms. The accuracy of program has\nimproved slightly as more healthcare professionals upload patient photos to the\napp. There are now some 150,000 images in its database.<\/p>\n\n\n\n<p><strong>Silos\nand bias<\/strong><\/p>\n\n\n\n<p>However the algorithm is only as good as its training data set, and there\u2019s a danger in case of rare disorders that affect only small numbers of people worldwide, the companies and researchers will commence to silo and commodify their data sets. And ethnic bias in training data sets that contain mostly Caucasian faces remains an issue. A 2017 study&nbsp;of children with an intellectual disability found that whereas Face2Gene\u2019s recognition rate for <a href=\"https:\/\/www.delveinsight.com\/report-store\/down-syndrome-market-insights-epidemiology-and-market-forecast\">Down syndrome<\/a> was <strong>80% <\/strong>among white Belgian children, it was just 37% for black Congolese children. With a more-diverse training data set, however, the algorithm\u2019s accuracy for African faces improved, showing that more-equitable representation of diverse populations is achievable. The issue will soon be addressed and less and less bias will be there.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A range of rare genetic disorders is pinpointed by a deep-learning algorithm. This helps doctors and researchers by analysing pictures of people\u2019s faces. Researchers describe the technology after the diagnostic aid, a smartphone app called Face2Gene. It relies on machine-learning algorithmsand brain-like neural networks . This is for classification of distinctive facial features in people\u2019s [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":3915,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_editorskit_title_hidden":false,"_editorskit_reading_time":2,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","advgb_blocks_editor_width":"","advgb_blocks_columns_visual_guide":"","footnotes":""},"categories":[17],"tags":[2387,2385,2384,2382,2388,2375,1935],"industry":[17225],"therapeutic_areas":[17238,17234],"class_list":["post-3913","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","tag-angelman-syndrome","tag-artificial-intelligence-ai","tag-congenital","tag-deep-learning-algorithm","tag-fdna","tag-machine-learning-algorithms","tag-neurodevelopmental-disorders","industry-pharmaceutical","therapeutic_areas-genetic-disorders","therapeutic_areas-rare-diseases"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.8 (Yoast SEO v25.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Artificial Intelligence App Analyzes the Rare Genetic Disorders<\/title>\n<meta name=\"description\" content=\"A range of rare genetic disorders is pinpointed by a deep-learning algorithm. This helps doctors and researchers.........\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.delveinsight.com\/blog\/artificial-intelligence-face-scanning-app-spots-signs-of-rare-genetic-disorders\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Artificial Intelligence App Analyzes the Rare Genetic Disorders\" \/>\n<meta property=\"og:description\" content=\"A range of rare genetic disorders is pinpointed by a deep-learning algorithm. 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href=\"https:\/\/www.delveinsight.com\/blog\/articles\/\" class=\"advgb-post-tax-term\">Articles<\/a>"],"unlinked":["<span class=\"advgb-post-tax-term\">Articles<\/span>"]},"tags":{"linked":["<a href=\"https:\/\/www.delveinsight.com\/blog\/articles\/\" class=\"advgb-post-tax-term\">Angelman syndrome<\/a>","<a href=\"https:\/\/www.delveinsight.com\/blog\/articles\/\" class=\"advgb-post-tax-term\">artificial intelligence (AI)<\/a>","<a href=\"https:\/\/www.delveinsight.com\/blog\/articles\/\" class=\"advgb-post-tax-term\">congenital<\/a>","<a href=\"https:\/\/www.delveinsight.com\/blog\/articles\/\" class=\"advgb-post-tax-term\">deep-learning algorithm<\/a>","<a href=\"https:\/\/www.delveinsight.com\/blog\/articles\/\" class=\"advgb-post-tax-term\">FDNA<\/a>","<a href=\"https:\/\/www.delveinsight.com\/blog\/articles\/\" class=\"advgb-post-tax-term\">machine learning algorithms<\/a>","<a href=\"https:\/\/www.delveinsight.com\/blog\/articles\/\" class=\"advgb-post-tax-term\">neurodevelopmental disorders<\/a>"],"unlinked":["<span class=\"advgb-post-tax-term\">Angelman syndrome<\/span>","<span class=\"advgb-post-tax-term\">artificial intelligence (AI)<\/span>","<span class=\"advgb-post-tax-term\">congenital<\/span>","<span class=\"advgb-post-tax-term\">deep-learning algorithm<\/span>","<span class=\"advgb-post-tax-term\">FDNA<\/span>","<span class=\"advgb-post-tax-term\">machine learning algorithms<\/span>","<span class=\"advgb-post-tax-term\">neurodevelopmental disorders<\/span>"]}},"comment_count":"0","relative_dates":{"created":"Posted 7 years ago","modified":"Updated 3 years ago"},"absolute_dates":{"created":"Posted on Jan 9, 2019","modified":"Updated on Jun 14, 2023"},"absolute_dates_time":{"created":"Posted on Jan 9, 2019 4:29 pm","modified":"Updated on Jun 14, 2023 12:09 pm"},"featured_img_caption":"","series_order":"","_links":{"self":[{"href":"https:\/\/www.delveinsight.com\/blog\/wp-json\/wp\/v2\/posts\/3913","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.delveinsight.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.delveinsight.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.delveinsight.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.delveinsight.com\/blog\/wp-json\/wp\/v2\/comments?post=3913"}],"version-history":[{"count":0,"href":"https:\/\/www.delveinsight.com\/blog\/wp-json\/wp\/v2\/posts\/3913\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.delveinsight.com\/blog\/wp-json\/wp\/v2\/media\/3915"}],"wp:attachment":[{"href":"https:\/\/www.delveinsight.com\/blog\/wp-json\/wp\/v2\/media?parent=3913"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.delveinsight.com\/blog\/wp-json\/wp\/v2\/categories?post=3913"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.delveinsight.com\/blog\/wp-json\/wp\/v2\/tags?post=3913"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/www.delveinsight.com\/blog\/wp-json\/wp\/v2\/industry?post=3913"},{"taxonomy":"therapeutic_areas","embeddable":true,"href":"https:\/\/www.delveinsight.com\/blog\/wp-json\/wp\/v2\/therapeutic_areas?post=3913"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}